Clinical Focus


  • Diagnostic Radiology

Academic Appointments


  • Clinical Instructor, Radiology

Honors & Awards


  • Research Training Fellowship, HHMI (2009-10)
  • Student Travel Award, RSNA (2017)
  • Cancer Imaging Training Program, Stanford (2017-18)
  • Fellow Grant, RSNA (2017-19)
  • Collaborative Research Grant, Stanford Society of Physician Scholars (2018)
  • GPU Grant, NVIDIA (2018)
  • Power Science Magna Cum Laude Award, Society of Abdominal Radiology (2018)

Professional Education


  • Board Certification: Diagnostic Radiology, American Board of Radiology (2018)
  • Board Certification, American Board of Radiology, Diagnostic Radiology (2018)
  • Residency:University of Pittsburgh Radiology Residency (2017) PA
  • Internship:Abington Memorial Hospital (2013) PAUnited States of America
  • Medical Education:Stanford University School of Medicine Registrar (2012) CA
  • Doctor of Medicine, Stanford University, MED-MD (2012)
  • Bachelor of Arts, Harvard University, Biochemical Sciences (2007)

Current Research and Scholarly Interests


My current two greatest areas of interest are medical imaging informatics and molecular imaging. As part of an RSNA Fellow grant, I will use machine learning and texture analysis to develop a quantitative tool for the early detection of ovarian cancer using BR55, a novel molecular imaging agent that targets sites of neoangiogenesis. I am interested in additional opportunities to apply deep learning techniques, as well as correlating radiologic and pathologic data.

All Publications


  • Effect of CYP4F2, VKORC1, and CYP2C9 in Influencing Coumarin Dose: A Single-Patient Data Meta-Analysis in More Than 15,000 Individuals CLINICAL PHARMACOLOGY & THERAPEUTICS Danese, E., Raimondi, S., Montagnana, M., Tagetti, A., Langaee, T., Borgiani, P., Ciccacci, C., Carcas, A. J., Borobia, A. M., Tong, H. Y., Davila-Fajardo, C., Botton, M., Bourgeois, S., Deloukas, P., Caldwell, M. D., Burmester, J. K., Berg, R. L., Cavallari, L. H., Drozda, K., Huang, M., Zhao, L., Cen, H., Gonzalez-Conejero, R., Roldan, V., Nakamura, Y., Mushiroda, T., Gong, I. Y., Kim, R. B., Hirai, K., Itoh, K., Isaza, C., Beltran, L., Jimenez-Varo, E., Canadas-Garre, M., Giontella, A., Kringen, M. K., Haug, K., Gwak, H., Lee, K., Minuz, P., Lee, M., Lubitz, S. A., Scott, S., Mazzaccara, C., Sacchetti, L., Genc, E., Ozer, M., Pathare, A., Krishnamoorthy, R., Paldi, A., Siguret, V., Loriot, M., Kutala, V., Suarez-Kurtz, G., Perini, J., Denny, J. C., Ramirez, A. H., Mittal, B., Rathore, S., Sagreiya, H., Altman, R., Shahin, M. A., Khalifa, S., Limdi, N. A., Rivers, C., Shendre, A., Dillon, C., Suriapranata, I. M., Zhou, H., Tan, S., Tatarunas, V., Lesauskaite, V., Zhang, Y., Maitland-van der Zee, A. H., Verhoef, T., de Boer, A., Taljaard, M., Zambon, C., Pengo, V., Zhang, J., Pirmohamed, M., Johnson, J. A., Fava, C. 2019; 105 (6): 1477–91

    View details for DOI 10.1002/cpt.1323

    View details for Web of Science ID 000467751900030

  • Point Shear Wave Elastography Using Machine Learning to Differentiate Renal Cell Carcinoma and Angiomyolipoma. Ultrasound in medicine & biology Sagreiya, H., Akhbardeh, A., Li, D., Sigrist, R., Chung, B. I., Sonn, G. A., Tian, L., Rubin, D. L., Willmann, J. K. 2019

    Abstract

    The question of whether ultrasound point shear wave elastography can differentiate renal cell carcinoma (RCC) from angiomyolipoma (AML) is controversial. This study prospectively enrolled 51 patients with 52 renal tumors (42 RCCs, 10 AMLs). We obtained 10 measurements of shear wave velocity (SWV) in the renal tumor, cortex and medulla. Median SWV was first used to classify RCC versus AML. Next, the prediction accuracy of 4 machine learning algorithms-logistic regression, naive Bayes, quadratic discriminant analysis and support vector machines (SVMs)-was evaluated, using statistical inputs from the tumor, cortex and combined statistical inputs from tumor, cortex and medulla. After leave-one-out cross validation, models were evaluated using the area under the receiver operating characteristic (ROC) curve (AUC). Tumor median SWV performed poorly (AUC = 0.62; p = 0.23). Except logistic regression, all machine learning algorithms reached statistical significance using combined statistical inputs (AUC = 0.78-0.98; p < 7.1 * 10-3). SVMs demonstrated 94% accuracy (AUC = 0.98; p = 3.13 * 10-6) and clearly outperformed median SWV in differentiating RCC from AML (p = 2.8 * 10-4).

    View details for DOI 10.1016/j.ultrasmedbio.2019.04.009

    View details for PubMedID 31133445

  • Automatic inference of BI-RADS final assessment categories from narrative mammography report findings Journal of Biomedical Informatics Banerjee, I., Bozkurt, S., Alkim, E., Sagreiya, H., Kurian, A. W., Rubin, D. L. 2019
  • Automatic Inference of BI-RADS Final Assessment Categories from Narrative Mammography Report Findings. Journal of biomedical informatics Banerjee, I., Bozkurt, S., Alkim, E., Sagreiya, H., Kurian, A. W., Rubin, D. L. 2019: 103137

    Abstract

    We propose an efficient natural language processing approach for inferring the BI-RADS final assessment categories by analyzing only the mammogram findings reported by the mammographer in narrative form. The proposed hybrid method integrates semantic term embedding with distributional semantics, producing a context-aware vector representation of unstructured mammography reports. A large corpus of unannotated mammography reports (300,000) was used to learn the context of the key-terms using a distributional semantics approach, and the trained model was applied to generate context-aware vector representations of the reports annotated with BI-RADS category(22,091). The vectorized reports were utilized to train a supervised classifier to derive the BI-RADS assessment class. Even though the majority of the proposed embedding pipeline is unsupervised, the classifier was able to recognize substantial semantic information for deriving the BI-RADS categorization not only on a holdout internal testset and also on an external validation set (1,900 reports). Our proposed method outperforms a recently published domain-specific rule-based system and could be relevant for evaluating concordance between radiologists. With minimal requirement for task specific customization, the proposed method can be easily transferable to a different domain to support large scale text mining or derivation of patient phenotype.

    View details for PubMedID 30807833

  • A Multi-Model Framework to Estimate Perfusion Parameters Using Contrast-Enhanced Ultrasound Imaging. Medical physics Akhbardeh, A., Sagreiya, H., El Kaffas, A., Willmann, J. K., Rubin, D. L. 2018

    Abstract

    PURPOSE: Contrast-enhanced ultrasound imaging (CEUS) has expanded the diagnostic potential of ultrasound by enabling real-time imaging and quantification of tissue perfusion. Several perfusion models and curve fitting methods have been developed to quantify the temporal behavior of tracer signal and standardize perfusion quantification. While the least-squares approach has traditionally been applied for curve-fitting, it can be inadequate for noisy and complex data. Moreover, previous research suggests that certain perfusion models may be more relevant depending on the organ or tissue imaged. We propose a multi-model framework to select the most appropriate perfusion model and curve fitting method for each diagnostic application.METHODS: Our multi-model approach uses a system identification method, which estimates perfusion parameters from the model with the best fit to a given time-intensity curve (TIC). We compared current perfusion quantification methods that use a single perfusion model and curve fitting method and our proposed multi-model framework on bolus 3D dynamic contrast-enhanced ultrasound (DCE-US) in vivo images obtained in mice implanted with a colon cancer, as well as on simulation data. The quality of fit in estimating perfusion parameters was evaluated using the Spearman correlation coefficient, the coefficient of determination (R2 ), and the normalized root mean square error (NRMSE) to ensure that the multi-model framework finds the best perfusion model and curve fitting algorithm.RESULTS: Our multi-model framework outperforms conventional single perfusion model approaches with least squares optimization, providing more robust perfusion parameter estimation. R2 and NRMSE are 0.98 and 0.18 respectively for our proposed method. By comparison, the performance of the traditional approach is much more dependent upon the selection of the appropriate model. The R2 and NRMSE are 0.91 and 0.31, respectively.CONCLUSIONS: The proposed multi-model framework for perfusion-modeling outperforms the current approach of single perfusion modeling using least-squares optimization and more robustly estimates perfusion parameters when using empiric data labelled by an expert as the gold standard. Our technique is minimally sensitive to issues affecting the accuracy of perfusion parameter estimation, including rise time, noise, ROI size, and frame rate. This framework could be of key utility in modeling different perfusion systems in different tissues and organs. This article is protected by copyright. All rights reserved.

    View details for PubMedID 30554408

  • The effect of CYP4F2, VKORC1 and CYP2C9 in influencing coumarin dose. A single patient data meta-analysis in more than 15,000 individuals. Clinical pharmacology and therapeutics Danese, E., Raimondi, S., Montagnana, M., Tagetti, A., Langaee, T., Borgiani, P., Ciccacci, C., Carcas, A. J., Borobia, A. M., Tong, H. Y., Davila-Fajardo, C., Botton, M. R., Bourgeois, S., Deloukas, P., Caldwell, M. D., Burmester, J. K., Berg, R. L., Cavallari, L. H., Drozda, K., Huang, M., Zhao, L., Cen, H., Gonzalez-Conejero, R., Roldan, V., Nakamura, Y., Mushiroda, T., Gong, I. Y., Kim, R. B., Hirai, K., Itoh, K., Isaza, C., Beltran, L., Jimenez-Varo, E., Canadas-Garre, M., Giontella, A., Kringen, M. K., Bente Foss Haug, K., Gwak, H. S., Lee, K. E., Minuz, P., Lee, M. T., Lubitz, S. A., Scott, S., Mazzaccara, C., Sacchetti, L., Genc, E., Ozer, M., Pathare, A., Krishnamoorthy, R., Paldi, A., Siguret, V., Loriot, M., Kutala, V. K., Suarez-Kurtz, G., Perini, J., Denny, J. C., Ramirez, A. H., Mittal, B., Rathore, S. S., Sagreiya, H., Altman, R., Shahin, M. H., Khalifa, S. I., Limdi, N. A., Rivers, C., Shendre, A., Dillon, C., Suriapranata, I. M., Zhou, H., Tan, S., Tatarunas, V., Lesauskaite, V., Zhang, Y., Maitland-van der Zee, A. H., Verhoef, T. I., de Boer, A., Taljaard, M., Zambon, C. F., Pengo, V., Zhang, J. E., Pirmohamed, M., Johnson, J. A., Fava, C. 2018

    Abstract

    The CYP4F2 gene is known to influence mean coumarin dose. The aim of the present study was to undertake a meta-analysis at individual patients' level to capture the possible effect of ethnicity, gene-gene interaction or other drugs on the association and to verify if inclusion of CYP4F2*3 variant into dosing algorithms improves the prediction of mean coumarin dose. We asked the authors of our previous meta-analysis (30 articles) and of 38 new articles retrieved by a systematic review to send us individual patients' data. The final collection consists 15,754 patients split into a derivation and validation cohort. The CYP4F2*3 polymorphism was consistently associated with an increase in mean coumarin dose (+9% (95%CI 7-10%), with a higher effect in females, in patients taking acenocoumarol and in Whites. The inclusion of the CYP4F2*3 in dosing algorithms slightly improved the prediction of stable coumarin dose. New pharmacogenetic equations potentially useful for clinical practice were derived. This article is protected by copyright. All rights reserved.

    View details for PubMedID 30506689

  • A radiogenomic analysis of hepatocellular carcinoma: association between fractional allelic imbalance rate index and the liver imaging reporting and data system (LI-RADS) categories and features. The British journal of radiology Furlan, A., Almusa, O., Yu, R. K., Sagreiya, H., Borhani, A. A., Bae, K. T., Marsh, J. W. 2018: 20170962

    Abstract

    To evaluate the association between the liver imaging reporting and data system (LI-RADS) categories and features and the fractional allelic imbalance (FAI) rate index of hepatocellular carcinoma (HCC).The institutional review board approved this retrospective study. Medical records collected between January 2008 and December 2013 were reviewed to find patients with histologically confirmed HCC, FAI analysis, and CT or MR imaging of the liver. The final population included 71 patients (54 males, 17 females). Three radiologists reviewed the images using the LI-RADS v. 2014. The association between FAI and LI-RADS categories and features was tested using the Spearman's rank correlation coefficient (rho) and the Wilcoxon rank-sum test [low FAI (<40%) vs high FAI (≥40%)]. A p value < 0.007 was used as the threshold for statistical significance after application of the Bonferroni correction for multiple comparisons.HCCs were classified as LR-3 (n = 4), LR-4 (n = 22), and LR-5 (n = 45). There was a positive correlation (rho = 0.264) between FAI rate index and LI-RADS category, although not statistically significant after Bonferroni correction (p = 0.024). 14 of the 20 (70%) HCCs with high FAI (≥40%) were categorized as LR-5, 6/20 (30%) as LR-4 and none as LR-3 (p = 0.377). Among the evaluated LI-RADS imaging features, only lesion size showed a statistically significant different distribution in tumors with high FAI compared to those with low FAI. HCCs with FAI ≥40% were larger (56 ± 42 mm) compared to those with FAI <40% (36 ± 30 mm; p = 0.005).There was a positive correlation, although not statistically significant, between the LI-RADS diagnostic categories and the FAI rate of HCC. Tumors with high FAI were larger compared to those with low FAI. Advances in knowledge: HCCs with high (≥40%) FAI are larger compared to those with low (<40%) FAI.

    View details for PubMedID 29565672

  • Differences in Antipsychotic-Related Adverse Events in Adult, Pediatric, and Geriatric Populations. Cureus Sagreiya, H., Chen, Y., Kumarasamy, N. A., Ponnusamy, K., Chen, D., Das, A. K. 2017; 9 (2)

    Abstract

    In recent years, antipsychotic medications have increasingly been used in pediatric and geriatric populations, despite the fact that many of these drugs were approved based on clinical trials in adult patients only. Preliminary studies have shown that the "off-label" use of these drugs in pediatric and geriatric populations may result in adverse events not found in adults. In this study, we utilized the large-scale U.S. Food and Drug Administration (FDA) Adverse Events Reporting System (AERS) database to look at differences in adverse events from antipsychotics among adult, pediatric, and geriatric populations. We performed a systematic analysis of the FDA AERS database using MySQL by standardizing the database using structured terminologies and ontologies. We compared adverse event profiles of atypical versus typical antipsychotic medications among adult (18-65), pediatric (age < 18), and geriatric (> 65) populations. We found statistically significant differences between the number of adverse events in the pediatric versus adult populations with aripiprazole, clozapine, fluphenazine, haloperidol, olanzapine, quetiapine, risperidone, and thiothixene, and between the geriatric versus adult populations with aripiprazole, chlorpromazine, clozapine, fluphenazine, haloperidol, paliperidone, promazine, risperidone, thiothixene, and ziprasidone (p < 0.05, with adjustment for multiple comparisons). Furthermore, the particular types of adverse events reported also varied significantly between each population for aripiprazole, clozapine, haloperidol, olanzapine, quetiapine, risperidone, and ziprasidone (Chi-square, p < 10(-6)). Diabetes was the most commonly reported side effect in the adult population, compared to behavioral problems in the pediatric population and neurologic symptoms in the geriatric population. We also found discrepancies between the frequencies of reports in AERS and in the literature. Our analysis of the FDA AERS database shows that there are significant differences in both the numbers and types of adverse events among these age groups and between atypical and typical antipsychotics. It is important for clinicians to be mindful of these differences when prescribing antipsychotics, especially when prescribing medications off-label.

    View details for DOI 10.7759/cureus.1059

    View details for PubMedID 28465867

  • Genetic variants associated with warfarin dose in African-American individuals: a genome-wide association study. Lancet Perera, M. A., Cavallari, L. H., Limdi, N. A., Gamazon, E. R., Konkashbaev, A., Daneshjou, R., Pluzhnikov, A., Crawford, D. C., Wang, J., Liu, N., Tatonetti, N., Bourgeois, S., Takahashi, H., Bradford, Y., Burkley, B. M., Desnick, R. J., Halperin, J. L., Khalifa, S. I., Langaee, T. Y., Lubitz, S. A., Nutescu, E. A., Oetjens, M., Shahin, M. H., Patel, S. R., Sagreiya, H., Tector, M., Weck, K. E., Rieder, M. J., Scott, S. A., Wu, A. H., Burmester, J. K., Wadelius, M., Deloukas, P., Wagner, M. J., Mushiroda, T., Kubo, M., Roden, D. M., Cox, N. J., Altman, R. B., Klein, T. E., Nakamura, Y., Johnson, J. A. 2013; 382 (9894): 790-796

    Abstract

    BACKGROUND: VKORC1 and CYP2C9 are important contributors to warfarin dose variability, but explain less variability for individuals of African descent than for those of European or Asian descent. We aimed to identify additional variants contributing to warfarin dose requirements in African Americans. METHODS: We did a genome-wide association study of discovery and replication cohorts. Samples from African-American adults (aged ≥18 years) who were taking a stable maintenance dose of warfarin were obtained at International Warfarin Pharmacogenetics Consortium (IWPC) sites and the University of Alabama at Birmingham (Birmingham, AL, USA). Patients enrolled at IWPC sites but who were not used for discovery made up the independent replication cohort. All participants were genotyped. We did a stepwise conditional analysis, conditioning first for VKORC1 -1639G→A, followed by the composite genotype of CYP2C9*2 and CYP2C9*3. We prespecified a genome-wide significance threshold of p<5×10(-8) in the discovery cohort and p<0·0038 in the replication cohort. FINDINGS: The discovery cohort contained 533 participants and the replication cohort 432 participants. After the prespecified conditioning in the discovery cohort, we identified an association between a novel single nucleotide polymorphism in the CYP2C cluster on chromosome 10 (rs12777823) and warfarin dose requirement that reached genome-wide significance (p=1·51×10(-8)). This association was confirmed in the replication cohort (p=5·04×10(-5)); analysis of the two cohorts together produced a p value of 4·5×10(-12). Individuals heterozygous for the rs12777823 A allele need a dose reduction of 6·92 mg/week and those homozygous 9·34 mg/week. Regression analysis showed that the inclusion of rs12777823 significantly improves warfarin dose variability explained by the IWPC dosing algorithm (21% relative improvement). INTERPRETATION: A novel CYP2C single nucleotide polymorphism exerts a clinically relevant effect on warfarin dose in African Americans, independent of CYP2C9*2 and CYP2C9*3. Incorporation of this variant into pharmacogenetic dosing algorithms could improve warfarin dose prediction in this population. FUNDING: National Institutes of Health, American Heart Association, Howard Hughes Medical Institute, Wisconsin Network for Health Research, and the Wellcome Trust.

    View details for DOI 10.1016/S0140-6736(13)60681-9

    View details for PubMedID 23755828

  • Pathway analysis of genome-wide data improves warfarin dose prediction BMC GENOMICS Daneshjou, R., Tatonetti, N. P., Karczewski, K. J., Sagreiya, H., Bourgeois, S., Drozda, K., Burmester, J. K., Tsunoda, T., Nakamura, Y., Kubo, M., Tector, M., Limdi, N. A., Cavallari, L. H., Perera, M., Johnson, J. A., Klein, T. E., Altman, R. B. 2013; 14

    Abstract

    Many genome-wide association studies focus on associating single loci with target phenotypes. However, in the setting of rare variation, accumulating sufficient samples to assess these associations can be difficult. Moreover, multiple variations in a gene or a set of genes within a pathway may all contribute to the phenotype, suggesting that the aggregation of variations found over the gene or pathway may be useful for improving the power to detect associations.Here, we present a method for aggregating single nucleotide polymorphisms (SNPs) along biologically relevant pathways in order to seek genetic associations with phenotypes. Our method uses all available genetic variants and does not remove those in linkage disequilibrium (LD). Instead, it uses a novel SNP weighting scheme to down-weight the contributions of correlated SNPs. We apply our method to three cohorts of patients taking warfarin: two European descent cohorts and an African American cohort. Although the clinical covariates and key pharmacogenetic loci for warfarin have been characterized, our association metric identifies a significant association with mutations distributed throughout the pathway of warfarin metabolism. We improve dose prediction after using all known clinical covariates and pharmacogenetic variants in VKORC1 and CYP2C9. In particular, we find that at least 1% of the missing heritability in warfarin dose may be due to the aggregated effects of variations in the warfarin metabolic pathway, even though the SNPs do not individually show a significant association.Our method allows researchers to study aggregative SNP effects in an unbiased manner by not preselecting SNPs. It retains all the available information by accounting for LD-structure through weighting, which eliminates the need for LD pruning.

    View details for DOI 10.1186/1471-2164-14-S3-S11

    View details for Web of Science ID 000319869500011

    View details for PubMedID 23819817

  • Pathway analysis of genome-wide data improves warfarin dose prediction. BMC genomics Daneshjou, R., Tatonetti, N. P., Karczewski, K. J., Sagreiya, H., Bourgeois, S., Drozda, K., Burmester, J. K., Tsunoda, T., Nakamura, Y., Kubo, M., Tector, M., Limdi, N. A., Cavallari, L. H., Perera, M., Johnson, J. A., Klein, T. E., Altman, R. B. 2013; 14: S11-?

    Abstract

    Many genome-wide association studies focus on associating single loci with target phenotypes. However, in the setting of rare variation, accumulating sufficient samples to assess these associations can be difficult. Moreover, multiple variations in a gene or a set of genes within a pathway may all contribute to the phenotype, suggesting that the aggregation of variations found over the gene or pathway may be useful for improving the power to detect associations.Here, we present a method for aggregating single nucleotide polymorphisms (SNPs) along biologically relevant pathways in order to seek genetic associations with phenotypes. Our method uses all available genetic variants and does not remove those in linkage disequilibrium (LD). Instead, it uses a novel SNP weighting scheme to down-weight the contributions of correlated SNPs. We apply our method to three cohorts of patients taking warfarin: two European descent cohorts and an African American cohort. Although the clinical covariates and key pharmacogenetic loci for warfarin have been characterized, our association metric identifies a significant association with mutations distributed throughout the pathway of warfarin metabolism. We improve dose prediction after using all known clinical covariates and pharmacogenetic variants in VKORC1 and CYP2C9. In particular, we find that at least 1% of the missing heritability in warfarin dose may be due to the aggregated effects of variations in the warfarin metabolic pathway, even though the SNPs do not individually show a significant association.Our method allows researchers to study aggregative SNP effects in an unbiased manner by not preselecting SNPs. It retains all the available information by accounting for LD-structure through weighting, which eliminates the need for LD pruning.

    View details for DOI 10.1186/1471-2164-14-S3-S11

    View details for PubMedID 23819817

  • Impact of the CYP4F2 p.V433M Polymorphism on Coumarin Dose Requirement: Systematic Review and Meta-Analysis CLINICAL PHARMACOLOGY & THERAPEUTICS Danese, E., Montagnana, M., Johnson, J. A., Rettie, A. E., Zambon, C. F., Lubitz, S. A., Suarez-Kurtz, G., Cavallari, L. H., Zhao, L., Huang, M., Nakamura, Y., Mushiroda, T., Kringen, M. K., Borgiani, P., Ciccacci, C., Au, N. T., Langaee, T., Siguret, V., Loriot, M. A., Sagreiya, H., Altman, R. B., Shahin, M. H., Scott, S. A., Khalifa, S. I., Chowbay, B., Suriapranata, I. M., Teichert, M., Stricker, B. H., Taljaard, M., Botton, M. R., Zhang, J. E., Pirmohamed, M., Zhang, X., Carlquist, J. F., Horne, B. D., Lee, M. T., Pengo, V., Guidi, G. C., Minuz, P., Fava, C. 2012; 92 (6): 746-756

    Abstract

    A systematic review and a meta-analysis were performed to quantify the accumulated information from genetic association studies investigating the impact of the CYP4F2 rs2108622 (p.V433M) polymorphism on coumarin dose requirement. An additional aim was to explore the contribution of the CYP4F2 variant in comparison with, as well as after stratification for, the VKORC1 and CYP2C9 variants. Thirty studies involving 9,470 participants met prespecified inclusion criteria. As compared with CC-homozygotes, T-allele carriers required an 8.3% (95% confidence interval (CI): 5.6-11.1%; P < 0.0001) higher mean daily coumarin dose than CC homozygotes to reach a stable international normalized ratio (INR). There was no evidence of publication bias. Heterogeneity among studies was present (I(2) = 43%). Our results show that the CYP4F2 p.V433M polymorphism is associated with interindividual variability in response to coumarin drugs, but with a low effect size that is confirmed to be lower than those contributed by VKORC1 and CYP2C9 polymorphisms.

    View details for DOI 10.1038/clpt.2012.184

    View details for Web of Science ID 000311283400016

    View details for PubMedID 23132553

  • An integrative method for scoring candidate genes from association studies: application to warfarin dosing AMIA Summit on Translational Bioinformatics Tatonetti, N. P., Dudley, J. T., Sagreiya, H., Butte, A. J., Altman, R. B. BIOMED CENTRAL LTD. 2010

    Abstract

    A key challenge in pharmacogenomics is the identification of genes whose variants contribute to drug response phenotypes, which can include severe adverse effects. Pharmacogenomics GWAS attempt to elucidate genotypes predictive of drug response. However, the size of these studies has severely limited their power and potential application. We propose a novel knowledge integration and SNP aggregation approach for identifying genes impacting drug response. Our SNP aggregation method characterizes the degree to which uncommon alleles of a gene are associated with drug response. We first use pre-existing knowledge sources to rank pharmacogenes by their likelihood to affect drug response. We then define a summary score for each gene based on allele frequencies and train linear and logistic regression classifiers to predict drug response phenotypes.We applied our method to a published warfarin GWAS data set comprising 181 individuals. We find that our method can increase the power of the GWAS to identify both VKORC1 and CYP2C9 as warfarin pharmacogenes, where the original analysis had only identified VKORC1. Additionally, we find that our method can be used to discriminate between low-dose (AUROC=0.886) and high-dose (AUROC=0.764) responders.Our method offers a new route for candidate pharmacogene discovery from pharmacogenomics GWAS, and serves as a foundation for future work in methods for predictive pharmacogenomics.

    View details for Web of Science ID 000290218700009

    View details for PubMedID 21044367

    View details for PubMedCentralID PMC2967750

  • VKORC1 Pharmacogenomics Summary PHARMACOGENETICS AND GENOMICS Owen, R. P., Gong, L., Sagreiya, H., Klein, T. E., Altman, R. B. 2010; 20 (10): 642-644

    View details for DOI 10.1097/FPC.0b013e32833433b6

    View details for Web of Science ID 000281830900010

    View details for PubMedID 19940803

    View details for PubMedCentralID PMC3086043

  • The utility of general purpose versus specialty clinical databases for research: Warfarin dose estimation from extracted clinical variables JOURNAL OF BIOMEDICAL INFORMATICS Sagreiya, H., Altman, R. B. 2010; 43 (5): 747-751

    Abstract

    There is debate about the utility of clinical data warehouses for research. Using a clinical warfarin dosing algorithm derived from research-quality data, we evaluated the data quality of both a general-purpose database and a coagulation-specific database. We evaluated the functional utility of these repositories by using data extracted from them to predict warfarin dose. We reasoned that high-quality clinical data would predict doses nearly as accurately as research data, while poor-quality clinical data would predict doses less accurately. We evaluated the Mean Absolute Error (MAE) in predicted weekly dose as a metric of data quality. The MAE was comparable between the clinical gold standard (10.1mg/wk) and the specialty database (10.4 mg/wk), but the MAE for the clinical warehouse was 40% greater (14.1mg/wk). Our results indicate that the research utility of clinical data collected in focused clinical settings is greater than that of data collected during general-purpose clinical care.

    View details for DOI 10.1016/j.jbi.2010.03.014

    View details for Web of Science ID 000281927200010

    View details for PubMedID 20363365

    View details for PubMedCentralID PMC2928873

  • Extending and evaluating a warfarin dosing algorithm that includes CYP4F2 and pooled rare variants of CYP2C9 PHARMACOGENETICS AND GENOMICS Sagrieya, H., Berube, C., Wen, A., Ramakrishnan, R., Mir, A., Hamilton, A., Altman, R. B. 2010; 20 (7): 407-413

    Abstract

    Warfarin dosing remains challenging because of its narrow therapeutic window and large variability in dose response. We sought to analyze new factors involved in its dosing and to evaluate eight dosing algorithms, including two developed by the International Warfarin Pharmacogenetics Consortium (IWPC).we enrolled 108 patients on chronic warfarin therapy and obtained complete clinical and pharmacy records; we genotyped single nucleotide polymorphisms relevant to the VKORC1, CYP2C9, and CYP4F2 genes using integrated fluidic circuits made by Fluidigm.When applying the IWPC pharmacogenetic algorithm to our cohort of patients, the percentage of patients within 1 mg/d of the therapeutic warfarin dose increases from 54% to 63% using clinical factors only, or from 38% using a fixed-dose approach. CYP4F2 adds 4% to the fraction of the variability in dose (R) explained by the IWPC pharmacogenetic algorithm (P<0.05). Importantly, we show that pooling rare variants substantially increases the R for CYP2C9 (rare variants: P=0.0065, R=6%; common variants: P=0.0034, R=7%; rare and common variants: P=0.00018; R=12%), indicating that relatively rare variants not genotyped in genome-wide association studies may be important. In addition, the IWPC pharmacogenetic algorithm and the Gage (2008) algorithm perform best (IWPC: R=50%; Gage: R=49%), and all pharmacogenetic algorithms outperform the IWPC clinical equation (R=22%). VKORC1 and CYP2C9 genotypes did not affect long-term variability in dose. Finally, the Fluidigm platform, a novel warfarin genotyping method, showed 99.65% concordance between different operators and instruments.CYP4F2 and pooled rare variants of CYP2C9 significantly improve the ability to estimate warfarin dose.

    View details for DOI 10.1097/FPC.0b013e328338bac2

    View details for Web of Science ID 000278879400001

    View details for PubMedID 20442691

    View details for PubMedCentralID PMC3098751

  • Warfarin pharmacogenetics: a single VKORC1 polymorphism is predictive of dose across 3 racial groups BLOOD Limdi, N. A., Wadelius, M., Cavallari, L., Eriksson, N., Crawford, D. C., Lee, M. M., Chen, C., Motsinger-Reif, A., Sagreiya, H., Liu, N., Wu, A. H., Gage, B. F., Jorgensen, A., Pirmohamed, M., Shin, J., Suarez-Kurtz, G., Kimmel, S. E., Johnson, J. A., Klein, T. E., Wagner, M. J. 2010; 115 (18): 3827-3834

    Abstract

    Warfarin-dosing algorithms incorporating CYP2C9 and VKORC1 -1639G>A improve dose prediction compared with algorithms based solely on clinical and demographic factors. However, these algorithms better capture dose variability among whites than Asians or blacks. Herein, we evaluate whether other VKORC1 polymorphisms and haplotypes explain additional variation in warfarin dose beyond that explained by VKORC1 -1639G>A among Asians (n = 1103), blacks (n = 670), and whites (n = 3113). Participants were recruited from 11 countries as part of the International Warfarin Pharmacogenetics Consortium effort. Evaluation of the effects of individual VKORC1 single nucleotide polymorphisms (SNPs) and haplotypes on warfarin dose used both univariate and multi variable linear regression. VKORC1 -1639G>A and 1173C>T individually explained the greatest variance in dose in all 3 racial groups. Incorporation of additional VKORC1 SNPs or haplotypes did not further improve dose prediction. VKORC1 explained greater variability in dose among whites than blacks and Asians. Differences in the percentage of variance in dose explained by VKORC1 across race were largely accounted for by the frequency of the -1639A (or 1173T) allele. Thus, clinicians should recognize that, although at a population level, the contribution of VKORC1 toward dose requirements is higher in whites than in nonwhites; genotype predicts similar dose requirements across racial groups.

    View details for DOI 10.1182/blood-2009-12-255992

    View details for Web of Science ID 000277335900027

    View details for PubMedID 20203262

    View details for PubMedCentralID PMC2865873

  • Clinical assessment incorporating a personal genome LANCET Ashley, E. A., Butte, A. J., Wheeler, M. T., Chen, R., Klein, T. E., Dewey, F. E., Dudley, J. T., Ormond, K. E., Pavlovic, A., Morgan, A. A., Pushkarev, D., Neff, N. F., Hudgins, L., Gong, L., Hodges, L. M., Berlin, D. S., Thorn, C. F., Sangkuhl, K., Hebert, J. M., Woon, M., Sagreiya, H., Whaley, R., Knowles, J. W., Chou, M. F., Thakuria, J. V., Rosenbaum, A. M., Zaranek, A. W., Church, G. M., Greely, H. T., Quake, S. R., Altman, R. B. 2010; 375 (9725): 1525-1535

    Abstract

    The cost of genomic information has fallen steeply, but the clinical translation of genetic risk estimates remains unclear. We aimed to undertake an integrated analysis of a complete human genome in a clinical context.We assessed a patient with a family history of vascular disease and early sudden death. Clinical assessment included analysis of this patient's full genome sequence, risk prediction for coronary artery disease, screening for causes of sudden cardiac death, and genetic counselling. Genetic analysis included the development of novel methods for the integration of whole genome and clinical risk. Disease and risk analysis focused on prediction of genetic risk of variants associated with mendelian disease, recognised drug responses, and pathogenicity for novel variants. We queried disease-specific mutation databases and pharmacogenomics databases to identify genes and mutations with known associations with disease and drug response. We estimated post-test probabilities of disease by applying likelihood ratios derived from integration of multiple common variants to age-appropriate and sex-appropriate pre-test probabilities. We also accounted for gene-environment interactions and conditionally dependent risks.Analysis of 2.6 million single nucleotide polymorphisms and 752 copy number variations showed increased genetic risk for myocardial infarction, type 2 diabetes, and some cancers. We discovered rare variants in three genes that are clinically associated with sudden cardiac death-TMEM43, DSP, and MYBPC3. A variant in LPA was consistent with a family history of coronary artery disease. The patient had a heterozygous null mutation in CYP2C19 suggesting probable clopidogrel resistance, several variants associated with a positive response to lipid-lowering therapy, and variants in CYP4F2 and VKORC1 that suggest he might have a low initial dosing requirement for warfarin. Many variants of uncertain importance were reported.Although challenges remain, our results suggest that whole-genome sequencing can yield useful and clinically relevant information for individual patients.National Institute of General Medical Sciences; National Heart, Lung And Blood Institute; National Human Genome Research Institute; Howard Hughes Medical Institute; National Library of Medicine, Lucile Packard Foundation for Children's Health; Hewlett Packard Foundation; Breetwor Family Foundation.

    View details for Web of Science ID 000277655100025

    View details for PubMedID 20435227

  • Estimation of the Warfarin Dose with Clinical and Pharmacogenetic Data NEW ENGLAND JOURNAL OF MEDICINE Klein, T. E., Altman, R. B., Eriksson, N., Gage, B. F., Kimmel, S. E., Lee, M. M., Limdi, N. A., Page, D., Roden, D. M., Wagner, M. J., Caldwell, M. D., Johnson, J. A., Chen, Y. T., Wen, M. S., Caraco, Y., Achache, I., Blotnick, S., Muszkat, M., Shin, J. G., Kim, H. S., Suarez-Kurtz, G., Perini, J. A., Silva-Assuncao, E., Anderson, J. L., Horne, B. D., Carlquist, J. F., Caldwell, M. D., Berg, R. L., Burmester, J. K., Goh, B. C., Lee, S. C., Kamali, F., Sconce, E., Daly, A. K., Wu, A. H., Langaee, T. Y., Feng, H., Cavallari, L., Momary, K., Pirmohamed, M., Jorgensen, A., Toh, C. H., Williamson, P., McLeod, H., Evans, J. P., Weck, K. E., Brensinger, C., Nakamura, Y., Mushiroda, T., Veenstra, D., Meckley, L., Rieder, M. J., Rettie, A. E., Wadelius, M., Melhus, H., Stein, C. M., Schwartz, U., Kurnik, D., Deych, E., Lenzini, P., Eby, C., Chen, L. Y., Deloukas, P., Motsinger-Reif, A., Sagreiya, H., Srinivasan, B. S., Lantz, E., Chang, T., Ritchie, M., Lu, L. S., Shin, J. G. 2009; 360 (8): 753-764

    Abstract

    Genetic variability among patients plays an important role in determining the dose of warfarin that should be used when oral anticoagulation is initiated, but practical methods of using genetic information have not been evaluated in a diverse and large population. We developed and used an algorithm for estimating the appropriate warfarin dose that is based on both clinical and genetic data from a broad population base.Clinical and genetic data from 4043 patients were used to create a dose algorithm that was based on clinical variables only and an algorithm in which genetic information was added to the clinical variables. In a validation cohort of 1009 subjects, we evaluated the potential clinical value of each algorithm by calculating the percentage of patients whose predicted dose of warfarin was within 20% of the actual stable therapeutic dose; we also evaluated other clinically relevant indicators.In the validation cohort, the pharmacogenetic algorithm accurately identified larger proportions of patients who required 21 mg of warfarin or less per week and of those who required 49 mg or more per week to achieve the target international normalized ratio than did the clinical algorithm (49.4% vs. 33.3%, P<0.001, among patients requiring < or = 21 mg per week; and 24.8% vs. 7.2%, P<0.001, among those requiring > or = 49 mg per week).The use of a pharmacogenetic algorithm for estimating the appropriate initial dose of warfarin produces recommendations that are significantly closer to the required stable therapeutic dose than those derived from a clinical algorithm or a fixed-dose approach. The greatest benefits were observed in the 46.2% of the population that required 21 mg or less of warfarin per week or 49 mg or more per week for therapeutic anticoagulation.

    View details for Web of Science ID 000263411300005

    View details for PubMedID 19228618

    View details for PubMedCentralID PMC2722908

  • A general framework for dose optimization. AMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium Turcott, R. G., Sagreiya, H., Ashley, E. A., Altman, R. B., Das, A. K. 2009; 2009: 656-660

    Abstract

    Dose optimization is a ubiquitous challenge in clinical practice and includes both pharmacologic and non-pharmacologic interventions. Methods for the statistical assessment of optimum dosing are lacking. We developed a generic framework for dose titration and demonstrated its application in two domains. Optimum warfarin dose was estimated from clinical titration data. In addition, cardiac pacemaker interval optimization was conducted using three conventional techniques. For both data types, optima were obtained from mathematical functions fit to the raw data. The precision of the estimated optima was quantified using bootstrapping. In pacing optimization, the observed precision varied significantly among the techniques, suggesting that impedance cardiography is superior to commonly used echocardiographic methods. The average 95% confidence interval of the estimated optimum warfarin dose was +/-18%, suggesting that titration within this range is of limited utility. By identifying statistically ineffective interventions, objective analysis of optimization data may both improve outcomes and reduce healthcare costs.

    View details for PubMedID 20351936

  • The double-anchoring theory of lightness perception: A comment on Bressan (2006) PSYCHOLOGICAL REVIEW Howe, P. D., Sagreiya, H., Curtis, D. L., Zheng, C., Livingstone, M. S. 2007; 114 (4): 1105-1109

    Abstract

    Recently, a double-anchoring theory (DAT) of lightness perception was proposed (P. Bressan, 2006), which offers explanations for all the data explained by the original anchoring theory (A. Gilchrist et al., 1999), as well as a number of additional lightness phenomena. Consequently, DAT can account for an unprecedented range of empirical results, potentially explaining everything from the basic simultaneous contrast display to subtle variations of the Gelb effect. In this comment, the authors raised 4 concerns that demonstrate serious theoretical and empirical difficulties for DAT.

    View details for DOI 10.1037/0033-295X.114.4.1105

    View details for Web of Science ID 000249738000014

    View details for PubMedID 17907879

    View details for PubMedCentralID PMC2635063

  • Explaining the footsteps, belly dancer, Wenceslas, and kickback illusions JOURNAL OF VISION Howe, P. D., Thompson, P. G., Anstis, S. M., Sagreiya, H., Livingstone, M. S. 2006; 6 (12): 1396-1405

    Abstract

    The footsteps illusion (FI) demonstrates that an object's background can have a profound effect on the object's perceived speed. This illusion consists of a yellow bar and a blue bar that move over a black-and-white, striped background. Although the bars move at a constant rate, they appear to repeatedly accelerate and decelerate in antiphase with each other. Previously, this illusion has been explained in terms of the variations in contrast at the leading and trailing edges of the bars that occur as the bars traverse the striped background. Here, we show that this explanation is inadequate and instead propose that for each bar, the bar's leading edge, trailing edge, lateral edges, and the surrounding background edges all contribute to the bar's perceived speed and that the degree to which each edge contributes to the motion percept is determined by that edge's contrast. We show that this theory can explain all the data on the FI as well as the belly dancer and Wenceslas illusions. We conclude by presenting a new illusion, the kickback illusion, which, although geometrically similar to the FI, is mediated by a different mechanism, namely, reverse phi motion.

    View details for DOI 10.1167/6.12.5

    View details for Web of Science ID 000244603600005

    View details for PubMedID 17209742

    View details for PubMedCentralID PMC2637218